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    <title>MaplePrimes - Newest Questions</title>
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    <lastBuildDate>Tue, 14 Apr 2026 14:54:25 GMT</lastBuildDate>
    <pubDate>Tue, 14 Apr 2026 14:54:25 GMT</pubDate>
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    <description>The questions most recently asked on MaplePrimes</description>
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      <title>MaplePrimes - Newest Questions</title>
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    <item>
      <title>Numerical evaluation of a complex integral solution</title>
      <link>http://www.mapleprimes.com/questions/243541-Numerical-Evaluation-Of-A-Complex-Integral?ref=Feed:MaplePrimes:New%20Questions</link>
      <itunes:summary>&lt;p&gt;Consider a soil moisture transport problem with parameters: a = 9.88\times10^{-5} m/s,&amp;nbsp; b = -0.0065\ m&lt;sup&gt;3&lt;/sup&gt;/m&lt;sup&gt;3&lt;/sup&gt;, and D = 3.51*10^{-7} m&lt;sup&gt;2&lt;/sup&gt;/s. The length is L=0.08 m, and no-flux (outflow) is assumed, meaning q=0. In this case &amp;theta;&lt;sub&gt;0&lt;/sub&gt;= 0.355$ and&amp;nbsp;&amp;theta;&lt;span style="font-size: 10.5px;"&gt;L&lt;/span&gt;&amp;nbsp;= 0.10.&lt;/p&gt;

&lt;p&gt;A solution is expressed as a contour integral involving a complex-valued function V(k,x,t) and&amp;nbsp;evaluated numerically in Maple using parameterized contours k1(r),k2(r),k3(s).&lt;/p&gt;

&lt;p&gt;However, when this code is executed, Maple takes an extremely long time and appears to run indefinitely.&lt;/p&gt;

&lt;p&gt;Could you suggest an alternative numerical or analytical approach ( that could improve efficiency when evaluating this type of contour integral?&lt;/p&gt;



&lt;pre class="prettyprint"&gt;
a := 988/10000000;
b := -65/10000;
d := 351/1000000000;
q := 0;
theta0 := 355/1000;
thetaL := 1/10;
L := 8/100;
mu := a*(theta0 + b)/d;
c := a*(thetaL + b)/d;

mu := a*(theta0 + b)/d;
c := a*(thetaL + b)/d;

V := 1/(2*Pi)*exp(-d*k^2*t)*((exp(k*x*I)(mu - k*I) - exp(-mu*L)*exp(k*(L - x)*I)*(mu + L*I))*k*cos(k*L)/(k^2 + mu^2) + c*sin(k*L) - exp(k*L*I)(k*I + c)*sin(k*x)*(1 - exp(-mu*L)*exp(-I*k*L))/(mu + k*I) - (k*cos(k(L - x)) + c*sin(k(L - x)))*(1 - exp(-mu*L)*exp(k*L*I))/(mu - k*I) + 2*I*k*d*(k*cos(k(L - x)) + c*sin(k(L - x)))/(d*k^2 + a*q/d))/(k*cos(k*L) + c*sin(k*L)):

l := 5;
k1 := r -&amp;gt; l + I + r*exp(1/6*I*Pi);
k2 := r -&amp;gt; -l + I + r*exp(5/6*I*Pi);
k3 := s -&amp;gt; s + I;

dk1 := D(k1);
dk2 := D(k2);
dk3 := D(k3);

integrand1 := Re(eval(V, k = k1(r))*dk1(r) + eval(V, k = k2(r))*dk2(r));
integrand3 := Re(eval(V, k = k3(s))*dk3(s));
integrand2 := simplify(evalc(integrand1));

integrand4 := simplify(evalc(integrand3));
approx_u := proc(x, t) local temp1, temp2; temp1 := Int(eval(integrand2, [:-x = x, :-t = t]), r = 0 .. infinity, method = _d01amc); temp2 := Int(eval(integrand4, [:-x = x, :-t = t]), s = -L .. L, method = _d01ajc); evalf(temp1 + temp2); end proc;
&lt;/pre&gt;
</itunes:summary>
      <description>&lt;p&gt;Consider a soil moisture transport problem with parameters: a = 9.88\times10^{-5} m/s,&amp;nbsp; b = -0.0065\ m&lt;sup&gt;3&lt;/sup&gt;/m&lt;sup&gt;3&lt;/sup&gt;, and D = 3.51*10^{-7} m&lt;sup&gt;2&lt;/sup&gt;/s. The length is L=0.08 m, and no-flux (outflow) is assumed, meaning q=0. In this case &amp;theta;&lt;sub&gt;0&lt;/sub&gt;= 0.355$ and&amp;nbsp;&amp;theta;&lt;span style="font-size: 10.5px;"&gt;L&lt;/span&gt;&amp;nbsp;= 0.10.&lt;/p&gt;

&lt;p&gt;A solution is expressed as a contour integral involving a complex-valued function V(k,x,t) and&amp;nbsp;evaluated numerically in Maple using parameterized contours k1(r),k2(r),k3(s).&lt;/p&gt;

&lt;p&gt;However, when this code is executed, Maple takes an extremely long time and appears to run indefinitely.&lt;/p&gt;

&lt;p data-end="639" data-start="535"&gt;Could you suggest an alternative numerical or analytical approach ( that could improve efficiency when evaluating this type of contour integral?&lt;/p&gt;

&lt;h3 data-end="660" data-section-id="qkpmor" data-start="646"&gt;&lt;img 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" /&gt;&lt;/h3&gt;

&lt;pre class="prettyprint"&gt;
a := 988/10000000;
b := -65/10000;
d := 351/1000000000;
q := 0;
theta0 := 355/1000;
thetaL := 1/10;
L := 8/100;
mu := a*(theta0 + b)/d;
c := a*(thetaL + b)/d;

mu := a*(theta0 + b)/d;
c := a*(thetaL + b)/d;

V := 1/(2*Pi)*exp(-d*k^2*t)*((exp(k*x*I)(mu - k*I) - exp(-mu*L)*exp(k*(L - x)*I)*(mu + L*I))*k*cos(k*L)/(k^2 + mu^2) + c*sin(k*L) - exp(k*L*I)(k*I + c)*sin(k*x)*(1 - exp(-mu*L)*exp(-I*k*L))/(mu + k*I) - (k*cos(k(L - x)) + c*sin(k(L - x)))*(1 - exp(-mu*L)*exp(k*L*I))/(mu - k*I) + 2*I*k*d*(k*cos(k(L - x)) + c*sin(k(L - x)))/(d*k^2 + a*q/d))/(k*cos(k*L) + c*sin(k*L)):

l := 5;
k1 := r -&amp;gt; l + I + r*exp(1/6*I*Pi);
k2 := r -&amp;gt; -l + I + r*exp(5/6*I*Pi);
k3 := s -&amp;gt; s + I;

dk1 := D(k1);
dk2 := D(k2);
dk3 := D(k3);

integrand1 := Re(eval(V, k = k1(r))*dk1(r) + eval(V, k = k2(r))*dk2(r));
integrand3 := Re(eval(V, k = k3(s))*dk3(s));
integrand2 := simplify(evalc(integrand1));

integrand4 := simplify(evalc(integrand3));
approx_u := proc(x, t) local temp1, temp2; temp1 := Int(eval(integrand2, [:-x = x, :-t = t]), r = 0 .. infinity, method = _d01amc); temp2 := Int(eval(integrand4, [:-x = x, :-t = t]), s = -L .. L, method = _d01ajc); evalf(temp1 + temp2); end proc;
&lt;/pre&gt;
</description>
      <guid>243541</guid>
      <pubDate>Mon, 13 Apr 2026 22:44:18 Z</pubDate>
      <itunes:author>Paras31</itunes:author>
      <author>Paras31</author>
    </item>
    <item>
      <title>How to force change of units in ScatterPlot?</title>
      <link>http://www.mapleprimes.com/questions/242541-How-To-Force-Change-Of-Units-In-ScatterPlot?ref=Feed:MaplePrimes:New%20Questions</link>
      <itunes:summary>&lt;p&gt;Dear power users,&lt;/p&gt;

&lt;p&gt;In the attached document, I try to have DO2i in units of &amp;quot;mL/min/m&amp;sup2;&amp;quot; on the Y-axis of my plot. I have tried to obtain this with &amp;quot;convert&amp;quot;, but to no avail. Could anyone show me how to obtain the desired result?&lt;/p&gt;

&lt;p&gt;Thank you for your time&lt;a href="/view.aspx?sf=242541_question/PlotQuestionMaplePrimes.mw"&gt;PlotQuestionMaplePrimes.mw&lt;/a&gt;&lt;/p&gt;
</itunes:summary>
      <description>&lt;p&gt;Dear power users,&lt;/p&gt;

&lt;p&gt;In the attached document, I try to have DO2i in units of &amp;quot;mL/min/m&amp;sup2;&amp;quot; on the Y-axis of my plot. I have tried to obtain this with &amp;quot;convert&amp;quot;, but to no avail. Could anyone show me how to obtain the desired result?&lt;/p&gt;

&lt;p&gt;Thank you for your time&lt;a href="/view.aspx?sf=242541_question/PlotQuestionMaplePrimes.mw"&gt;PlotQuestionMaplePrimes.mw&lt;/a&gt;&lt;/p&gt;
</description>
      <guid>242541</guid>
      <pubDate>Mon, 13 Apr 2026 19:31:58 Z</pubDate>
      <itunes:author>FDS_ERT</itunes:author>
      <author>FDS_ERT</author>
    </item>
    <item>
      <title>Why is evaluation lacking in ExpressionTools:-Compare</title>
      <link>http://www.mapleprimes.com/questions/242540-Why-Is-Evaluation-Lacking-In-ExpressionTools?ref=Feed:MaplePrimes:New%20Questions</link>
      <itunes:summary>&lt;p&gt;Can someone explain why the element selection inside Compare works differently for the second argument?&lt;/p&gt;

&lt;form name="worksheet_form"&gt;&lt;input name="md.ref" type="hidden" value="02D675DD60BC2C51470E6FF9EB45543A"&gt;
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			&lt;td&gt;
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				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img align="middle" alt="ode := diff(diff(y(x), x), x)+sin(y(x)) = 0; IC := y(infinity) = Pi; sol := [dsolve([ode, IC])]" height="61" src="/view.aspx?sf=242540_question/6c80770ae60022808fc813e697ad1787.gif" style="vertical-align:-44px" width="768"&gt;&lt;/p&gt;
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			&lt;table&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;
						&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="diff(diff(y(x), x), x)+sin(y(x)) = 0" height="50" src="/view.aspx?sf=242540_question/068af8e51c60a881ec55605c4df3839b.gif" style="vertical-align:-20px" width="214"&gt;&lt;/p&gt;
						&lt;/td&gt;
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			&lt;table&gt;
				&lt;tbody&gt;
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						&lt;td&gt;
						&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="y(infinity) = Pi" height="26" src="/view.aspx?sf=242540_question/9919f06c88d237673d1afe0f8d86b7ad.gif" style="vertical-align:-7px" width="105"&gt;&lt;/p&gt;
						&lt;/td&gt;
						&lt;td align="right" style="color:#000000; font-family:Times, serif; font-weight:bold; font-style:normal;"&gt;&amp;nbsp;&lt;/td&gt;
					&lt;/tr&gt;
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			&lt;table&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;
						&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img align="middle" alt="[y(x) = RootOf(-(Int(1/(2*cos(_a)+RootOf(-(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. Pi))+_a+c__2))^(1/2), _a = 0 .. _Z))+x+c__2), y(x) = RootOf(Int(1/(2*cos(_a)+RootOf(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. Pi)+_a+c__2))^(1/2), _a = 0 .. _Z)+x+c__2)]" height="240" src="/view.aspx?sf=242540_question/fe8a4410e2236456343de60fcc3f2fed.gif" style="vertical-align:-181px" width="738"&gt;&lt;/p&gt;
						&lt;/td&gt;
						&lt;td align="right" style="color:#000000; font-family:Times, serif; font-weight:bold; font-style:normal;"&gt;(1)&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img alt="dsolve([ode, y(x__IC) = y__IC])" height="28" src="/view.aspx?sf=242540_question/40f1685045e223a1ee00d44676781996.gif" style="vertical-align:-11px" width="174"&gt;&lt;/p&gt;
						&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;table&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;
						&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img align="middle" alt="y(x) = RootOf(-(Int(1/(2*cos(_a)+RootOf(-(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC))+x__IC+c__2))^(1/2), _a = 0 .. _Z))+x+c__2), y(x) = RootOf(Int(1/(2*cos(_a)+RootOf(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC)+x__IC+c__2))^(1/2), _a = 0 .. _Z)+x+c__2)" height="258" src="/view.aspx?sf=242540_question/a67f8256cc3ccbb55c07100afecaa0b7.gif" style="vertical-align:-196px" width="738"&gt;&lt;/p&gt;
						&lt;/td&gt;
						&lt;td align="right" style="color:#000000; font-family:Times, serif; font-weight:bold; font-style:normal;"&gt;(2)&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img alt="ExpressionTools:-Compare(sol[1], [y(x) = RootOf(-(Int(1/(2*cos(_a)+RootOf(-(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC))+x__IC+c__2))^(1/2), _a = 0 .. _Z))+x+c__2), y(x) = RootOf(Int(1/(2*cos(_a)+RootOf(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC)+x__IC+c__2))^(1/2), _a = 0 .. _Z)+x+c__2)][1])" height="26" src="/view.aspx?sf=242540_question/29b5f4b4b766abf8d359646516db58e4.gif" style="vertical-align:-7px" width="768"&gt;&lt;/p&gt;
						&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;table&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;
						&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img align="middle" alt="&amp;quot;([[y(x)=RootOf(-(&amp;amp;int;)[0]^_Z1/(sqrt(2 cos(_a)+RootOf(-(&amp;amp;int;)[0]^Pi1/(sqrt(2 cos(_a)+_Z)) &amp;amp;DifferentialD;_a+_a+`c__2`))) &amp;amp;DifferentialD;_a+x+`c__2`)],[[y(x)=RootOf(-(&amp;amp;int;)[0]^_Z1/(sqrt(2 cos(_a)+RootOf(-(&amp;amp;int;)[0]^`y__IC`1/(sqrt(2 cos(_a)+_Z)) &amp;amp;DifferentialD;_a+`x__IC`+`c__2`))) &amp;amp;DifferentialD;_a+x+`c__2`),y(x)=RootOf((&amp;amp;int;)[0]^_Z1/(sqrt(2 cos(_a)+RootOf((&amp;amp;int;)[0]^`y__IC`1/(sqrt(2 cos(_a)+_Z)) &amp;amp;DifferentialD;_a+`x__IC`+`c__2`))) &amp;amp;DifferentialD;_a+x+`c__2`)][1]]])&amp;quot;" height="350" src="/view.aspx?sf=242540_question/b6833dcc893f533dd381f4a96b987cf4.gif" style="vertical-align:-446px" width="738"&gt;&lt;/p&gt;
						&lt;/td&gt;
						&lt;td align="right" style="color:#000000; font-family:Times, serif; font-weight:bold; font-style:normal;"&gt;(3)&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img alt="whattype(y(x) = RootOf(-(Int(1/(2*cos(_a)+RootOf(-(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC))+x__IC+c__2))^(1/2), _a = 0 .. _Z))+x+c__2), y(x) = RootOf(Int(1/(2*cos(_a)+RootOf(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC)+x__IC+c__2))^(1/2), _a = 0 .. _Z)+x+c__2))" height="26" src="/view.aspx?sf=242540_question/48ecbb8378db353b207b953540c337e5.gif" style="vertical-align:-7px" width="106"&gt;&lt;/p&gt;
						&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;table&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;
						&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="exprseq" height="23" src="/view.aspx?sf=242540_question/d85c0809d576bc62fc7fb953da5bafa0.gif" style="vertical-align:-6px" width="52"&gt;&lt;/p&gt;
						&lt;/td&gt;
						&lt;td align="right" style="color:#000000; font-family:Times, serif; font-weight:bold; font-style:normal;"&gt;(4)&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img alt="ExpressionTools:-Compare(sol[1], (y(x) = RootOf(-(Int(1/(2*cos(_a)+RootOf(-(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC))+x__IC+c__2))^(1/2), _a = 0 .. _Z))+x+c__2), y(x) = RootOf(Int(1/(2*cos(_a)+RootOf(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC)+x__IC+c__2))^(1/2), _a = 0 .. _Z)+x+c__2))[1])" height="26" src="/view.aspx?sf=242540_question/09db29b4624415c0b8167c3a9844da8a.gif" style="vertical-align:-7px" width="673"&gt;&lt;/p&gt;
						&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;table&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;
						&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img align="middle" alt="&amp;quot;([[y(x)=RootOf(-(&amp;amp;int;)[0]^_Z1/(sqrt(2 cos(_a)+RootOf(-(&amp;amp;int;)[0]^Pi1/(sqrt(2 cos(_a)+_Z)) &amp;amp;DifferentialD;_a+_a+`c__2`))) &amp;amp;DifferentialD;_a+x+`c__2`)],[(y(x)=RootOf(-(&amp;amp;int;)[0]^_Z1/(sqrt(2 cos(_a)+RootOf(-(&amp;amp;int;)[0]^`y__IC`1/(sqrt(2 cos(_a)+_Z)) &amp;amp;DifferentialD;_a+`x__IC`+`c__2`))) &amp;amp;DifferentialD;_a+x+`c__2`),y(x)=RootOf((&amp;amp;int;)[0]^_Z1/(sqrt(2 cos(_a)+RootOf((&amp;amp;int;)[0]^`y__IC`1/(sqrt(2 cos(_a)+_Z)) &amp;amp;DifferentialD;_a+`x__IC`+`c__2`))) &amp;amp;DifferentialD;_a+x+`c__2`))[1]]])&amp;quot;" height="349" src="/view.aspx?sf=242540_question/7cef7558dfe9fa8faddea8d39a8160e1.gif" style="vertical-align:-446px" width="738"&gt;&lt;/p&gt;
						&lt;/td&gt;
						&lt;td align="right" style="color:#000000; font-family:Times, serif; font-weight:bold; font-style:normal;"&gt;(5)&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img alt="NULL" height="23" src="/view.aspx?sf=242540_question/092b1e563c5f4ae0680c24e2896a21fa.gif" style="vertical-align:-6px" width="9"&gt;&lt;/p&gt;
						&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img align="middle" alt="(y(x) = RootOf(-(Int(1/(2*cos(_a)+RootOf(-(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC))+x__IC+c__2))^(1/2), _a = 0 .. _Z))+x+c__2), y(x) = RootOf(Int(1/(2*cos(_a)+RootOf(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC)+x__IC+c__2))^(1/2), _a = 0 .. _Z)+x+c__2))[1]; ExpressionTools:-Compare(sol[1], %)" height="44" src="/view.aspx?sf=242540_question/5f20253cf766afc448ce8db0b39bdccf.gif" style="vertical-align:-25px" width="768"&gt;&lt;/p&gt;

			&lt;table&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;
						&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="y(x) = RootOf(-(Int(1/sqrt(2*cos(_a)+RootOf(-(Int(1/sqrt(2*cos(_a)+_Z), _a = 0 .. y__IC))+x__IC+c__2)), _a = 0 .. _Z))+x+c__2)" height="138" src="/view.aspx?sf=242540_question/5d0d56570f4540d02d9e0ea5231dc89b.gif" style="vertical-align:-74px" width="664"&gt;&lt;/p&gt;
						&lt;/td&gt;
						&lt;td align="right" style="color:#000000; font-family:Times, serif; font-weight:bold; font-style:normal;"&gt;(6)&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img alt="NULL" height="23" src="/view.aspx?sf=242540_question/f97996cb2271a05fface75c55fdd342a.gif" style="vertical-align:-6px" width="11"&gt;&lt;/p&gt;
						&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;
			&lt;/td&gt;
		&lt;/tr&gt;
	&lt;/tbody&gt;
&lt;/table&gt;
&lt;input name="sequence" type="hidden" value="1"&gt; &lt;input name="cmd" type="hidden" value="none"&gt;&lt;/form&gt;

&lt;p&gt;&lt;a href="/view.aspx?sf=242540_question/Compare_and_indexing_output.mw"&gt;Download Compare_and_indexing_output.mw&lt;/a&gt;&lt;/p&gt;
</itunes:summary>
      <description>&lt;p&gt;Can someone explain why the element selection inside Compare works differently for the second argument?&lt;/p&gt;

&lt;form name="worksheet_form"&gt;&lt;input name="md.ref" type="hidden" value="02D675DD60BC2C51470E6FF9EB45543A"&gt;
&lt;table align="center" width="768"&gt;
	&lt;tbody&gt;
		&lt;tr&gt;
			&lt;td&gt;
			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img align="middle" alt="ode := diff(diff(y(x), x), x)+sin(y(x)) = 0; IC := y(infinity) = Pi; sol := [dsolve([ode, IC])]" height="61" src="/view.aspx?sf=242540_question/6c80770ae60022808fc813e697ad1787.gif" style="vertical-align:-44px" width="768"&gt;&lt;/p&gt;
						&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;table&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;
						&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="diff(diff(y(x), x), x)+sin(y(x)) = 0" height="50" src="/view.aspx?sf=242540_question/068af8e51c60a881ec55605c4df3839b.gif" style="vertical-align:-20px" width="214"&gt;&lt;/p&gt;
						&lt;/td&gt;
						&lt;td align="right" style="color:#000000; font-family:Times, serif; font-weight:bold; font-style:normal;"&gt;&amp;nbsp;&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;table&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;
						&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="y(infinity) = Pi" height="26" src="/view.aspx?sf=242540_question/9919f06c88d237673d1afe0f8d86b7ad.gif" style="vertical-align:-7px" width="105"&gt;&lt;/p&gt;
						&lt;/td&gt;
						&lt;td align="right" style="color:#000000; font-family:Times, serif; font-weight:bold; font-style:normal;"&gt;&amp;nbsp;&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;table&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;
						&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img align="middle" alt="[y(x) = RootOf(-(Int(1/(2*cos(_a)+RootOf(-(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. Pi))+_a+c__2))^(1/2), _a = 0 .. _Z))+x+c__2), y(x) = RootOf(Int(1/(2*cos(_a)+RootOf(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. Pi)+_a+c__2))^(1/2), _a = 0 .. _Z)+x+c__2)]" height="240" src="/view.aspx?sf=242540_question/fe8a4410e2236456343de60fcc3f2fed.gif" style="vertical-align:-181px" width="738"&gt;&lt;/p&gt;
						&lt;/td&gt;
						&lt;td align="right" style="color:#000000; font-family:Times, serif; font-weight:bold; font-style:normal;"&gt;(1)&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img alt="dsolve([ode, y(x__IC) = y__IC])" height="28" src="/view.aspx?sf=242540_question/40f1685045e223a1ee00d44676781996.gif" style="vertical-align:-11px" width="174"&gt;&lt;/p&gt;
						&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;table&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;
						&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img align="middle" alt="y(x) = RootOf(-(Int(1/(2*cos(_a)+RootOf(-(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC))+x__IC+c__2))^(1/2), _a = 0 .. _Z))+x+c__2), y(x) = RootOf(Int(1/(2*cos(_a)+RootOf(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC)+x__IC+c__2))^(1/2), _a = 0 .. _Z)+x+c__2)" height="258" src="/view.aspx?sf=242540_question/a67f8256cc3ccbb55c07100afecaa0b7.gif" style="vertical-align:-196px" width="738"&gt;&lt;/p&gt;
						&lt;/td&gt;
						&lt;td align="right" style="color:#000000; font-family:Times, serif; font-weight:bold; font-style:normal;"&gt;(2)&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;table style="margin-left:0px;margin-right:0px"&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img alt="ExpressionTools:-Compare(sol[1], [y(x) = RootOf(-(Int(1/(2*cos(_a)+RootOf(-(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC))+x__IC+c__2))^(1/2), _a = 0 .. _Z))+x+c__2), y(x) = RootOf(Int(1/(2*cos(_a)+RootOf(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC)+x__IC+c__2))^(1/2), _a = 0 .. _Z)+x+c__2)][1])" height="26" src="/view.aspx?sf=242540_question/29b5f4b4b766abf8d359646516db58e4.gif" style="vertical-align:-7px" width="768"&gt;&lt;/p&gt;
						&lt;/td&gt;
					&lt;/tr&gt;
				&lt;/tbody&gt;
			&lt;/table&gt;

			&lt;table&gt;
				&lt;tbody&gt;
					&lt;tr valign="baseline"&gt;
						&lt;td&gt;
						&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img align="middle" alt="&amp;quot;([[y(x)=RootOf(-(&amp;amp;int;)[0]^_Z1/(sqrt(2 cos(_a)+RootOf(-(&amp;amp;int;)[0]^Pi1/(sqrt(2 cos(_a)+_Z)) &amp;amp;DifferentialD;_a+_a+`c__2`))) &amp;amp;DifferentialD;_a+x+`c__2`)],[[y(x)=RootOf(-(&amp;amp;int;)[0]^_Z1/(sqrt(2 cos(_a)+RootOf(-(&amp;amp;int;)[0]^`y__IC`1/(sqrt(2 cos(_a)+_Z)) &amp;amp;DifferentialD;_a+`x__IC`+`c__2`))) &amp;amp;DifferentialD;_a+x+`c__2`),y(x)=RootOf((&amp;amp;int;)[0]^_Z1/(sqrt(2 cos(_a)+RootOf((&amp;amp;int;)[0]^`y__IC`1/(sqrt(2 cos(_a)+_Z)) &amp;amp;DifferentialD;_a+`x__IC`+`c__2`))) &amp;amp;DifferentialD;_a+x+`c__2`)][1]]])&amp;quot;" height="350" src="/view.aspx?sf=242540_question/b6833dcc893f533dd381f4a96b987cf4.gif" style="vertical-align:-446px" width="738"&gt;&lt;/p&gt;
						&lt;/td&gt;
						&lt;td align="right" style="color:#000000; font-family:Times, serif; font-weight:bold; font-style:normal;"&gt;(3)&lt;/td&gt;
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					&lt;tr valign="baseline"&gt;
						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img alt="whattype(y(x) = RootOf(-(Int(1/(2*cos(_a)+RootOf(-(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC))+x__IC+c__2))^(1/2), _a = 0 .. _Z))+x+c__2), y(x) = RootOf(Int(1/(2*cos(_a)+RootOf(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC)+x__IC+c__2))^(1/2), _a = 0 .. _Z)+x+c__2))" height="26" src="/view.aspx?sf=242540_question/48ecbb8378db353b207b953540c337e5.gif" style="vertical-align:-7px" width="106"&gt;&lt;/p&gt;
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						&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="exprseq" height="23" src="/view.aspx?sf=242540_question/d85c0809d576bc62fc7fb953da5bafa0.gif" style="vertical-align:-6px" width="52"&gt;&lt;/p&gt;
						&lt;/td&gt;
						&lt;td align="right" style="color:#000000; font-family:Times, serif; font-weight:bold; font-style:normal;"&gt;(4)&lt;/td&gt;
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						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
						&lt;td&gt;
						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img alt="ExpressionTools:-Compare(sol[1], (y(x) = RootOf(-(Int(1/(2*cos(_a)+RootOf(-(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC))+x__IC+c__2))^(1/2), _a = 0 .. _Z))+x+c__2), y(x) = RootOf(Int(1/(2*cos(_a)+RootOf(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC)+x__IC+c__2))^(1/2), _a = 0 .. _Z)+x+c__2))[1])" height="26" src="/view.aspx?sf=242540_question/09db29b4624415c0b8167c3a9844da8a.gif" style="vertical-align:-7px" width="673"&gt;&lt;/p&gt;
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						&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img align="middle" alt="&amp;quot;([[y(x)=RootOf(-(&amp;amp;int;)[0]^_Z1/(sqrt(2 cos(_a)+RootOf(-(&amp;amp;int;)[0]^Pi1/(sqrt(2 cos(_a)+_Z)) &amp;amp;DifferentialD;_a+_a+`c__2`))) &amp;amp;DifferentialD;_a+x+`c__2`)],[(y(x)=RootOf(-(&amp;amp;int;)[0]^_Z1/(sqrt(2 cos(_a)+RootOf(-(&amp;amp;int;)[0]^`y__IC`1/(sqrt(2 cos(_a)+_Z)) &amp;amp;DifferentialD;_a+`x__IC`+`c__2`))) &amp;amp;DifferentialD;_a+x+`c__2`),y(x)=RootOf((&amp;amp;int;)[0]^_Z1/(sqrt(2 cos(_a)+RootOf((&amp;amp;int;)[0]^`y__IC`1/(sqrt(2 cos(_a)+_Z)) &amp;amp;DifferentialD;_a+`x__IC`+`c__2`))) &amp;amp;DifferentialD;_a+x+`c__2`))[1]]])&amp;quot;" height="349" src="/view.aspx?sf=242540_question/7cef7558dfe9fa8faddea8d39a8160e1.gif" style="vertical-align:-446px" width="738"&gt;&lt;/p&gt;
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						&lt;td align="right" style="color:#000000; font-family:Times, serif; font-weight:bold; font-style:normal;"&gt;(5)&lt;/td&gt;
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						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
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						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img alt="NULL" height="23" src="/view.aspx?sf=242540_question/092b1e563c5f4ae0680c24e2896a21fa.gif" style="vertical-align:-6px" width="9"&gt;&lt;/p&gt;
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			&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img align="middle" alt="(y(x) = RootOf(-(Int(1/(2*cos(_a)+RootOf(-(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC))+x__IC+c__2))^(1/2), _a = 0 .. _Z))+x+c__2), y(x) = RootOf(Int(1/(2*cos(_a)+RootOf(Int(1/(2*cos(_a)+_Z)^(1/2), _a = 0 .. y__IC)+x__IC+c__2))^(1/2), _a = 0 .. _Z)+x+c__2))[1]; ExpressionTools:-Compare(sol[1], %)" height="44" src="/view.aspx?sf=242540_question/5f20253cf766afc448ce8db0b39bdccf.gif" style="vertical-align:-25px" width="768"&gt;&lt;/p&gt;

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						&lt;p align="center" style="margin:0 0 0 0; padding-top:0px; padding-bottom:0px"&gt;&lt;img alt="y(x) = RootOf(-(Int(1/sqrt(2*cos(_a)+RootOf(-(Int(1/sqrt(2*cos(_a)+_Z), _a = 0 .. y__IC))+x__IC+c__2)), _a = 0 .. _Z))+x+c__2)" height="138" src="/view.aspx?sf=242540_question/5d0d56570f4540d02d9e0ea5231dc89b.gif" style="vertical-align:-74px" width="664"&gt;&lt;/p&gt;
						&lt;/td&gt;
						&lt;td align="right" style="color:#000000; font-family:Times, serif; font-weight:bold; font-style:normal;"&gt;(6)&lt;/td&gt;
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						&lt;td&gt;&lt;span style="color:#78000e;font-size: 100%;font-family: monospace,monospace;font-weight:bold;font-style:normal;"&gt;&amp;nbsp;&lt;/span&gt;&lt;/td&gt;
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						&lt;p align="left" style="margin:0 0 0 0; padding-top:3px; padding-bottom:3px"&gt;&lt;img alt="NULL" height="23" src="/view.aspx?sf=242540_question/f97996cb2271a05fface75c55fdd342a.gif" style="vertical-align:-6px" width="11"&gt;&lt;/p&gt;
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&lt;input name="sequence" type="hidden" value="1"&gt; &lt;input name="cmd" type="hidden" value="none"&gt;&lt;/form&gt;

&lt;p&gt;&lt;a href="/view.aspx?sf=242540_question/Compare_and_indexing_output.mw"&gt;Download Compare_and_indexing_output.mw&lt;/a&gt;&lt;/p&gt;
</description>
      <guid>242540</guid>
      <pubDate>Sat, 11 Apr 2026 18:14:12 Z</pubDate>
      <itunes:author>C_R</itunes:author>
      <author>C_R</author>
    </item>
    <item>
      <title>How much will the meadow cost?</title>
      <link>http://www.mapleprimes.com/questions/242539-How-Much-Will-The-Meadow-Cost?ref=Feed:MaplePrimes:New%20Questions</link>
      <itunes:summary>&lt;p&gt;With thanks to @sand and @janhardo for their help in solving a variational problem using Maple, I&amp;#39;m presenting a puzzle worth remembering. It was originally published in an IBM competition over 20 years ago under simpler conditions than those presented here, and later posed in a more challenging form in private circles:&lt;/p&gt;

&lt;p&gt;A farmer wants to buy a section of a large meadow/pasture for his sheep. He likes a lone, large oak tree on the meadow. This tree is to be the starting and ending point of the fence around the desired section. The fence is exactly 100 meters long. The farmer creates a sketch of the situation. He places the oak tree at the origin of a Cartesian coordinate system with a positive y-axis pointing north and sketches the estimated location of the desired meadow section east of the oak tree. The seller then stipulates that the price per square meter increases linearly eastward by a factor of 1/m&amp;sup3;. An agreement is reached, and the farmer now considers the optimal route for the fence to maximize the return on his investment.&lt;/p&gt;

&lt;p&gt;The fence route without corners&amp;nbsp;and the value of the purchased meadow must be calculated.&lt;/p&gt;
</itunes:summary>
      <description>&lt;p&gt;With thanks to @sand and @janhardo for their help in solving a variational problem using Maple, I&amp;#39;m presenting a puzzle worth remembering. It was originally published in an IBM competition over 20 years ago under simpler conditions than those presented here, and later posed in a more challenging form in private circles:&lt;/p&gt;

&lt;p&gt;A farmer wants to buy a section of a large meadow/pasture for his sheep. He likes a lone, large oak tree on the meadow. This tree is to be the starting and ending point of the fence around the desired section. The fence is exactly 100 meters long. The farmer creates a sketch of the situation. He places the oak tree at the origin of a Cartesian coordinate system with a positive y-axis pointing north and sketches the estimated location of the desired meadow section east of the oak tree. The seller then stipulates that the price per square meter increases linearly eastward by a factor of 1/m&amp;sup3;. An agreement is reached, and the farmer now considers the optimal route for the fence to maximize the return on his investment.&lt;/p&gt;

&lt;p&gt;The fence route without corners&amp;nbsp;and the value of the purchased meadow must be calculated.&lt;/p&gt;
</description>
      <guid>242539</guid>
      <pubDate>Sat, 11 Apr 2026 09:38:10 Z</pubDate>
      <itunes:author>Alfred_F</itunes:author>
      <author>Alfred_F</author>
    </item>
    <item>
      <title>A sphere with latitude and longitude circles</title>
      <link>http://www.mapleprimes.com/questions/242537-A-Sphere-With-Latitude-And-Longitude-Circles?ref=Feed:MaplePrimes:New%20Questions</link>
      <itunes:summary>&lt;p&gt;Dear Maple users&lt;/p&gt;

&lt;p&gt;I can easily draw a sphere with the following command:&lt;/p&gt;

&lt;p&gt;plot3d(1, t = -Pi .. Pi, p = 0 .. Pi, coords = spherical)&lt;/p&gt;

&lt;p&gt;&lt;span class="HwtZe"&gt;&lt;span class="jCAhz ChMk0b"&gt;&lt;span class="ryNqvb"&gt;Let&amp;#39;s say I want to change the number of latitude circles so that there is one for every 10 degrees?&lt;/span&gt;&lt;/span&gt; &lt;span class="jCAhz ChMk0b"&gt;&lt;span class="ryNqvb"&gt;Longitude circles are given for every 15 degrees, which is appropriate - corresponding to one for every hour!&lt;/span&gt;&lt;/span&gt; &lt;span class="jCAhz ChMk0b"&gt;&lt;span class="ryNqvb"&gt;But in general: Is there a way to change the number of circles of both types?&lt;/span&gt;&lt;/span&gt; &lt;span class="jCAhz ChMk0b"&gt;&lt;span class="ryNqvb"&gt;Or maybe you need to piece together a plot of several kinds of plots?&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

&lt;p&gt;&lt;span class="HwtZe"&gt;&lt;span class="jCAhz ChMk0b"&gt;&lt;span class="ryNqvb"&gt;Erik&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;
</itunes:summary>
      <description>&lt;p&gt;Dear Maple users&lt;/p&gt;

&lt;p&gt;I can easily draw a sphere with the following command:&lt;/p&gt;

&lt;p&gt;plot3d(1, t = -Pi .. Pi, p = 0 .. Pi, coords = spherical)&lt;/p&gt;

&lt;p&gt;&lt;span class="HwtZe" jsaction="mouseup:Sxi9L,BR6jm; mousedown:qjlr0e" jsname="jqKxS" lang="en"&gt;&lt;span class="jCAhz ChMk0b" jsaction="agoMJf:PFBcW;MZfLnc:P7O7bd;nt4Alf:pvnm0e,pfE8Hb,PFBcW;B01qod:dJXsye;H1e5u:iXtTIf;lYIUJf:hij5Wb;tSpjdb:qAKMYb" jscontroller="BiTO4b" jsname="txFAF"&gt;&lt;span class="ryNqvb" jsaction="click:PDNqTc,GFf3ac,qlVvte;contextmenu:Nqw7Te,QP7LD; mouseout:Nqw7Te; mouseover:PDNqTc,c2aHje" jsname="W297wb"&gt;Let&amp;#39;s say I want to change the number of latitude circles so that there is one for every 10 degrees?&lt;/span&gt;&lt;/span&gt; &lt;span class="jCAhz ChMk0b" jsaction="agoMJf:PFBcW;MZfLnc:P7O7bd;nt4Alf:pvnm0e,pfE8Hb,PFBcW;B01qod:dJXsye;H1e5u:iXtTIf;lYIUJf:hij5Wb;tSpjdb:qAKMYb" jscontroller="BiTO4b" jsname="txFAF"&gt;&lt;span class="ryNqvb" jsaction="click:PDNqTc,GFf3ac,qlVvte;contextmenu:Nqw7Te,QP7LD; mouseout:Nqw7Te; mouseover:PDNqTc,c2aHje" jsname="W297wb"&gt;Longitude circles are given for every 15 degrees, which is appropriate - corresponding to one for every hour!&lt;/span&gt;&lt;/span&gt; &lt;span class="jCAhz ChMk0b" jsaction="agoMJf:PFBcW;MZfLnc:P7O7bd;nt4Alf:pvnm0e,pfE8Hb,PFBcW;B01qod:dJXsye;H1e5u:iXtTIf;lYIUJf:hij5Wb;tSpjdb:qAKMYb" jscontroller="BiTO4b" jsname="txFAF"&gt;&lt;span class="ryNqvb" jsaction="click:PDNqTc,GFf3ac,qlVvte;contextmenu:Nqw7Te,QP7LD; mouseout:Nqw7Te; mouseover:PDNqTc,c2aHje" jsname="W297wb"&gt;But in general: Is there a way to change the number of circles of both types?&lt;/span&gt;&lt;/span&gt; &lt;span class="jCAhz ChMk0b" jsaction="agoMJf:PFBcW;MZfLnc:P7O7bd;nt4Alf:pvnm0e,pfE8Hb,PFBcW;B01qod:dJXsye;H1e5u:iXtTIf;lYIUJf:hij5Wb;tSpjdb:qAKMYb" jscontroller="BiTO4b" jsname="txFAF"&gt;&lt;span class="ryNqvb" jsaction="click:PDNqTc,GFf3ac,qlVvte;contextmenu:Nqw7Te,QP7LD; mouseout:Nqw7Te; mouseover:PDNqTc,c2aHje" jsname="W297wb"&gt;Or maybe you need to piece together a plot of several kinds of plots?&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;

&lt;p&gt;&lt;span class="HwtZe" jsaction="mouseup:Sxi9L,BR6jm; mousedown:qjlr0e" jsname="jqKxS" lang="en"&gt;&lt;span class="jCAhz ChMk0b" jsaction="agoMJf:PFBcW;MZfLnc:P7O7bd;nt4Alf:pvnm0e,pfE8Hb,PFBcW;B01qod:dJXsye;H1e5u:iXtTIf;lYIUJf:hij5Wb;tSpjdb:qAKMYb" jscontroller="BiTO4b" jsname="txFAF"&gt;&lt;span class="ryNqvb" jsaction="click:PDNqTc,GFf3ac,qlVvte;contextmenu:Nqw7Te,QP7LD; mouseout:Nqw7Te; mouseover:PDNqTc,c2aHje" jsname="W297wb"&gt;Erik&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;
</description>
      <guid>242537</guid>
      <pubDate>Fri, 10 Apr 2026 22:00:48 Z</pubDate>
      <itunes:author>erik10</itunes:author>
      <author>erik10</author>
    </item>
    <item>
      <title>Factorizing nested radicals</title>
      <link>http://www.mapleprimes.com/questions/242536-Factorizing-Nested-Radicals?ref=Feed:MaplePrimes:New%20Questions</link>
      <itunes:summary>&lt;p&gt;# The question is: how to get S knowing E?&amp;nbsp; All the attemps below after S assigned (suggested by Claude) fail factoring E.&amp;nbsp;&amp;nbsp;&lt;br&gt;
&lt;br&gt;
restart;&lt;/p&gt;

&lt;p&gt;interface(showassumed = 0):&amp;nbsp;&lt;br&gt;
assume(a&amp;gt;=0, b&amp;gt;=0, c&amp;gt;=0);&lt;br&gt;
assume(x::real, y::real,z::real);&lt;/p&gt;

&lt;p&gt;S := sqrt(x^2+(a+sqrt(y^2+(b+sqrt(z^2+c^2))^2))^2):&lt;br&gt;
E := expand(S):&lt;br&gt;
simplify(E,sqrt):&lt;br&gt;
combine(E,sqrt):&lt;br&gt;
simplify(combine(E, sqrt), sqrt):&lt;br&gt;
evalindets(E, &amp;#39;radical&amp;#39;, f -&amp;gt; simplify(f, sqrt)):&lt;br&gt;
simplify(radnormal(E), sqrt):&lt;/p&gt;
</itunes:summary>
      <description>&lt;p&gt;# The question is: how to get S knowing E?&amp;nbsp; All the attemps below after S assigned (suggested by Claude) fail factoring E.&amp;nbsp;&amp;nbsp;&lt;br /&gt;
&lt;br /&gt;
restart;&lt;/p&gt;

&lt;p&gt;interface(showassumed = 0):&amp;nbsp;&lt;br /&gt;
assume(a&amp;gt;=0, b&amp;gt;=0, c&amp;gt;=0);&lt;br /&gt;
assume(x::real, y::real,z::real);&lt;/p&gt;

&lt;p&gt;S := sqrt(x^2+(a+sqrt(y^2+(b+sqrt(z^2+c^2))^2))^2):&lt;br /&gt;
E := expand(S):&lt;br /&gt;
simplify(E,sqrt):&lt;br /&gt;
combine(E,sqrt):&lt;br /&gt;
simplify(combine(E, sqrt), sqrt):&lt;br /&gt;
evalindets(E, &amp;#39;radical&amp;#39;, f -&amp;gt; simplify(f, sqrt)):&lt;br /&gt;
simplify(radnormal(E), sqrt):&lt;/p&gt;
</description>
      <guid>242536</guid>
      <pubDate>Fri, 10 Apr 2026 16:21:44 Z</pubDate>
      <itunes:author>danielpf</itunes:author>
      <author>danielpf</author>
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